1. Field of the Invention
The present invention relates to machine vision systems and, more specifically, to identifying and locating instances of a shape under large variations in linear degrees of freedom and/or stroke widths in machine vision systems.
2. Background Information
The use of advanced machine vision systems and their underlying software is increasingly employed in a variety of manufacturing and quality control processes. Machine vision enables quicker, more accurate and repeatable results to be obtained in the production of both mass-produced and custom products. Basic machine vision systems include one or more cameras (typically having solid-state charge couple device (CCD) imaging elements) directed at an area of interest, frame grabber/image processing elements that capture and transmit CCD images, a computer and display for running the machine vision software application and manipulating the captured images, and appropriate illumination on the area of interest.
Many applications of machine vision involve the inspection of components and surfaces for defects that affect quality. Where sufficiently serious defects are noted, a part of a surface is marked as unacceptable/defective. Machine vision has also been employed in varying degrees to assist in manipulating manufacturing engines in the performance of specific tasks.
The advent of increasingly faster and higher-performance computers, has enabled the development of machine vision systems that employ powerful search tools. In general, advanced machine vision tools acquire an image of a pattern via a camera and analyze the outline or a particular part of the pattern, such as a predetermined fiducial mark. The processing speed of the underlying computer in which the tool resides is sufficient to enable a very large number of real time calculations to be completed in a short time frame. This particularly enables the search tool to determine the coordinates within an image reference system for each analyzed point in the viewed area, and correlate these through repetition with a desired pattern. The search tool may map the locations of various points in the captured image stored points in the model image, and determine whether the captured image points fall within an acceptable range of values relative to the model image points. Using various decision algorithms, the tool decides whether the viewed pattern, in a particular rotation and scale corresponds to the desired search pattern. If so, the tool confirms that the viewed pattern is, in fact, the pattern for which the tool is searching and fixes its position and orientation. An example of such a search tool is the PatMax® product available from Cognex Corporation of Natick, Mass.
Machine vision search tools often require precise training using one or more alignment models to identify the desired patterns and/or fiducials to be identified and located during run time. Fiducials may have a large number of linear degrees of freedom, e.g., rotation, translation, etc. Fiducials may also have a varying size of the stroke width associated therewith. Typically, the machine vision system would need to be trained with a plurality of images representing each of the variations within the degrees of freedom and/or stroke widths. This may substantially increase the time required to train a machine vision system. Furthermore, by including a large number of trained images in a machine vision system, the system may require additional processing time during run time to identify fiducials. What is needed is a technique for automatic training a machine vision system to identify fiducials under large variations of linear degrees of freedom and/or stroke widths.